- Referenced in 339 articles
- template encompassing a wide variety of Gaussian spatial process models for univariate as well...
- Referenced in 93 articles
- Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver...
- Referenced in 87 articles
- such as CART and random forest; Gaussian process models (Kriging), and combinations of di erent...
- Referenced in 58 articles
- latin hypercube and updates a Gaussian processes surrogate model of the search landscape after every...
- Referenced in 39 articles
- package tgp: Bayesian treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design ... treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also...
- Referenced in 38 articles
- Gaussian processes for machine learning (GPML) toolbox. The GPML toolbox provides a wide range ... functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance ... ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well...
- Referenced in 122 articles
- data. invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model ... Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based ... Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution...
- Referenced in 29 articles
- Gaussian Process (GP) framework written in python, from the Sheffield machine learning group. Gaussian processes...
- Referenced in 33 articles
- members, e.g. metamodeling (polynomial chaos expansions, Gaussian process modelling, a.k.a. Kriging, low-rank tensor approximations...
- Referenced in 19 articles
- package GPfit: Gaussian Processes Modeling. A computationally stable approach of fitting a Gaussian Process ... model to a deterministic simulator. Gaussian process (GP) models are commonly used statistical metamodels...
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- fast and flexible Python library for Gaussian Process (GP) Regression. A full introduction ... theory of Gaussian Processes is beyond the scope of this documentation but the best resource...
- Referenced in 40 articles
- toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA). This ... models that are based on log-Gaussian Cox processes and include local interaction in these...
- Referenced in 26 articles
- closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual...
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- package mlegp: Maximum Likelihood Estimates of Gaussian Processes. Maximum likelihood Gaussian process modeling for univariate...
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- toolbox is a versatile collection of Gaussian process models and computational tools required for inference...
- Referenced in 16 articles
- Gaussian Process Surrogate Approximate Bayesian Computation. Scientists often express their understanding of the world through ... obtained from every simulation in a Gaussian process which acts as a surrogate function...
- Referenced in 22 articles
- support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression...
- Referenced in 21 articles
- laGP: Local Approximate Gaussian Process Regression. Performs approximate GP regression for large computer experiments...
- Referenced in 16 articles
- space-time data using  Bayesian Gaussian Process (GP) Models,  Bayesian Auto-Regressive ... Models, and  Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal...
- Referenced in 14 articles
- GPflow: a Gaussian process library using tensorflow. GPflow is a Gaussian process library that uses...